{"id":"W7131999844","doi":"","title":"Tyrex: Improve Customer Acquisition with Data Insight","year":2020,"lang":"","type":"other","venue":"CEIBS Institutional Repository","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Casa","funders":"","keywords":"Data acquisition; Data collection; Knowledge acquisition; Quality (philosophy); Key (lock); Customer intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"category_scores_codex":[0.000572737,0.002424939,0.001856846,0.001067771,0.002169242,0.0007616279,0.004284909,0.001838966,0.004345967],"category_scores_gemma":[0.0002808806,0.002269391,0.0004083127,0.001764358,0.00486272,0.002505333,0.002549688,0.002656648,0.05033274],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002711173,"about_ca_system_score_gemma":0.0106332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002912767,"about_ca_topic_score_gemma":0.00006798256,"domain_scores_codex":[0.986792,0.0005997951,0.001942094,0.005204059,0.003996078,0.001465986],"domain_scores_gemma":[0.9894845,0.0001825373,0.002290609,0.005869658,0.0007693262,0.001403358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00690634,0.003717357,0.001143289,0.003051637,0.009370506,0.02011153,0.001160509,0.00180597,0.184636,0.05739245,0.7054137,0.005290744],"study_design_scores_gemma":[0.003169177,0.0004736202,0.001140415,0.002305465,0.00181484,0.00232492,0.00008618755,0.001728728,0.004609146,0.00003943439,0.9794769,0.002831169],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0005059626,0.00894022,0.002072773,0.0002901899,0.009656834,0.002763361,0.006745914,0.0008890704,0.9681357],"genre_scores_gemma":[0.4547584,0.0008494798,0.01011843,0.002408694,0.03430269,0.000622131,0.02876014,0.004727384,0.4634527],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.504683,"threshold_uncertainty_score":0.9996443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0217802005538234,"score_gpt":0.2464651653432562,"score_spread":0.2246849647894328,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}